Computer technology has been developed that a computer can perform many sophisticated tasks in fast speed. However, many tasks simple to human beings cannot be performed or are difficult for the computers to perform. It is because currently the computers cannot “think”.
The attempt to simulate the human thinking process is based on the computational theory of the mind. The existing methods in artificial intelligent field try to imitate the human “thinking” process by establishing a vast knowledge base and rule base. The problem is that most of the human thinking process cannot be reduced to mathematic formulas and models, and mathematic formulas and models are necessary for any automatic process driven by rules in the current technology field. The neural network was created to solve this problem by eliminating the needs for rules in the inner structure of the network. However, the neural network is based on fuzzy logic, the links between inputs and outputs are established based on the model inputs and outputs, wherein the accuracy between the inputs and outputs depends on the quality of the model inputs and outputs, the actual links are neither traceable, nor controllable. Therefore, the accuracy of the actual output cannot be guaranteed or even predicted. Obviously, neural network is not a true imitation of the human thinking process, especially in using languages. A solution is needed and long over due.
The problem in the existing knowledge and methods is mostly related to the confusion and misunderstanding or even fear or mind block about the mystery of human mind or thinking process. An example of this problem is the famous “Chinese Room” hypothetical adopted by philosopher John Searle. By this hypothetical, Searle refuted the computational theory of mind by questioning the notion of “understanding” by illustrating that simply knowing the link between one word with another word does not means the person understands the meaning of these words. No satisfying response was offered although the amounts of responses are impressive.
The confusion about “understanding” explained lack of progress in artificial intelligent filed. The founder of the computational theory of the mind, Hilary Putnam, had revised his opinion on this theory significantly. The computational theory of mind believed that the continuous linking process propagated from one set of symbols to other symbols would be a thinking process, in essence not fundamentally different from the ordinary mathematic computation process. But a word symbol must have meaning, and computational theory of mind cannot provide a computer usable representation of the meaning of the word symbol. At this point, attempts had been diverted to various directions (for example, Putnam had engaged himself in a discussion about truthfulness of a representation, which does not directly related to the meaning of word symbols or thinking processes, because the meaning of word symbols and thinking processes would be presumed to be true, and false meanings or thinking processes would simply be removed if discovered later on.)
The computational theory of mind also encountered difficulties in realization of the continuous linking processes between word symbols in computers. The linguistic analysis by Noam Chomsky regarding the sentence structures revealed the complicity and variety of the sentence structures, and is not successful for establishing linking processes between word symbols in general sense. Chomsky studied the sentence structures in the attempt to find innate structure of the languages, but not computer model base on his theory can reconstruct languages successfully.